Neumann Niklas D, Brauers Jur J, van Yperen Nico W, van der Linde Mees, Lemmink Koen A P M, Brink Michel S, Hasselman Fred, den Hartigh Ruud J R
Department of Psychology, Faculty of Behavioral and Social Sciences, University of Groningen, Groningen, The Netherlands.
Department of Human Movement Sciences, Faculty of Medical Sciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Sports Med Open. 2024 Dec 16;10(1):129. doi: 10.1186/s40798-024-00787-5.
There has been an increasing interest in the development and prevention of sports injuries from a complex dynamic systems perspective. From this perspective, injuries may occur following critical fluctuations in the psychophysiological state of an athlete. Our objective was to quantify these so-called Early Warning Signals (EWS) as a proof of concept to determine their explanatory performance for injuries. The sample consisted of 23 professional youth football (soccer) players. Self-reports of psychological and physiological factors as well as data from heart rate and GPS sensors were gathered on every training and match day over two competitive seasons, which resulted in an average of 339 observations per player (range = 155-430). We calculated the Dynamic Complexity (DC) index of these data, representing a metric of critical fluctuations. Next, we used this EWS to predict injuries (traumatic and overuse).
Results showed a significant peak of DC in 30% of the incurred injuries, in the six data points (roughly one and a half weeks) before the injury. The warning signal exhibited a specificity of 95%, that is, correctly classifying non-injury instances. We followed up on this promising result with additional calculations to account for the naturally imbalanced data (fewer injuries than non-injuries). The relatively low F we obtained (0.08) suggests that the model's overall ability to discriminate between injuries and non-injuries is rather poor, due to the high false positive rate.
By detecting critical fluctuations preceding one-third of the injuries, this study provided support for the complex systems theory of injuries. Furthermore, it suggests that increasing critical fluctuations may be seen as an EWS on which practitioners can intervene. Yet, the relatively high false positive rate on the entire data set, including periods without injuries, suggests critical fluctuations may also precede transitions to other (e.g., stronger) states. Future research should therefore dig deeper into the meaning of critical fluctuations in the psychophysiological states of athletes.
Complex Systems Theory suggests that sports injuries may be preceded by a warning signal characterized by a short window of increased critical fluctuations. Results of the current study showed such increased critical fluctuations before 30% of the injuries. Across the entire data set, we also found a considerable number of critical fluctuations that were not followed by an injury, suggesting that the warning signal may also precede transitions to other (e.g., healthier) states. Increased critical fluctuations may be interpreted as a window of opportunity for the practitioner to launch timely and targeted interventions, and researchers should dig deeper into the meaning of such fluctuations.
从复杂动态系统的角度来看，人们对运动损伤的发展和预防越来越感兴趣。从这个角度来看，运动员心理生理状态的临界波动之后可能会发生损伤。我们的目标是量化这些所谓的早期预警信号（EWS），作为一种概念验证，以确定它们对损伤的解释能力。样本包括23名职业青年足球运动员。在两个比赛赛季的每个训练和比赛日收集心理和生理因素的自我报告以及心率和GPS传感器的数据，每位运动员平均有339次观察数据（范围=155-430）。我们计算了这些数据的动态复杂性（DC）指数，它代表临界波动的一个指标。接下来，我们使用这个EWS来预测损伤（创伤性和过度使用性损伤）。
结果显示，在30%的损伤发生前的六个数据点（大约一周半），DC出现了显著峰值。该预警信号的特异性为95%，即能正确分类非损伤情况。我们对这个有前景的结果进行了额外计算，以考虑数据自然不平衡的情况（损伤比非损伤少）。我们得到的相对较低的F值（0.08）表明，由于假阳性率高，该模型区分损伤和非损伤的整体能力相当差。
通过检测三分之一损伤之前的临界波动，本研究为损伤的复杂系统理论提供了支持。此外，这表明临界波动增加可能被视为从业者可以进行干预的早期预警信号。然而，在整个数据集上，包括无损伤时期，相对较高的假阳性率表明临界波动也可能先于向其他（如更强壮）状态的转变。因此，未来的研究应该更深入地探究运动员心理生理状态中临界波动的意义。
复杂系统理论表明，运动损伤之前可能会出现一个预警信号，其特征是临界波动增加的时间窗口较短。本研究结果显示，在30%的损伤之前出现了这种临界波动增加的情况。在整个数据集中，我们还发现了相当数量的临界波动之后并没有发生损伤，这表明预警信号也可能先于向其他（如更健康）状态的转变。临界波动增加可以被解释为从业者及时进行有针对性干预的机会窗口，研究人员应该更深入地探究这种波动的意义。
